it is better to put this line before L30/L32 and make the logs more understandable? e.g.
before L30 VLOG(4) << "LabelSmoothInferSpmd call ElementwiseBinaryInferSpmd:"
before L32 VLOG(4) << "LabelSmoothInferSpmd call ElementwiseUnaryInferSpmd:"

Thanks!done

we need more unit test case to check:
if # shape: [16, 16, 16], [1, 16]. [0, -1, 1], [-1, 1], what result should be
if # shape: [16, 16, 16], [1, 16]. [0, -1, 1], [-1, 0], what result should be
if # shape: [16, 16, 16], [1, 16]. [0, -1, 1], [-1, -1], what result should be

In the second station # shape: [16, 16, 16], [1, 16]. [0, -1, 1], [-1, 0]
Because of reuse the ElementwiseBinaryInferSpmd, now it will cause error now when meet 1 and 0 with same axes (due to the ShardingMergeForAxis).
The following error is below, and I check the error now rather than implement it

 (Unimplemented) Tensor Axis[c] is Sharded by two different mesh dimension [0] and [1]. (at ../paddle/phi/infermeta/spmd_rules/utils.cc:70)

According to the calculation logic of this operator, if # shape: [16, 16, 16], [1, 16] and dims_mapping: [0, -1, 1], [-1, 0] the result of dims_mapping should be [0, -1, 1], [-1, 1], [0, -1, 1]? if so, we should add some necessary checks and modifications to dims_mapping of prior_dist before calling ElementwiseBinaryInferSpmd?

Yes. If # shape: [16, 16, 16], [1, 16] and dims_mapping: [0, -1, 1], [-1, 0] the result of dims_mapping should be [0, -1, 1], [-1, 1], [0, -1, 1]
If label's rank is greater than prior_dist's axis. It has an extra_dims_mapping. When prior_dist's dims_mappin in it, replicate it to -1 first. It will work better than before.
(In addition, I think in the future ElementwiseBinaryInferSpmd also should deal with this station

好的好的